What an AI system needs to last in production.
A live connection to the system of record.
Not an export. Yesterday's file is wrong about today.
A decision you can reconstruct.
You can see who saw what, when, and why.
The exceptions, not the clean cases.
They carry the risk, so a person sees them.
Someone on your staff who can change it.
Your team can change it without calling the vendor who built it.
Scope, build, run.
An AI system earns its keep once it runs every day, on real work. See pricing →
A call, then a scopeYou get a scope and a price.
We talk through the work. You get a written scope with the price to build it and the cost to run it each month, before any work starts.
- A written scope
- The build price
- The monthly run cost
A buildIt runs on your data.
We build on your data, behind your access controls, in your repository from the first week.
- Code in your repository
- Evaluations and regression tests
- A runbook
Capacity by the monthYou rank the list. We work it.
You rank the list. An engineer works through it in order, billed by the month.
- Your ranked list, worked in order
- One monthly bill
Managed operationsWe run it after launch.
We watch for drift, handle the exceptions and ship the releases, on a monthly retainer. We write the runbook and train your operators.
- Drift caught
- Exceptions handled
- Releases shipped
You own it when we leave.
The code is in your repository from week one. We write the runbook and train your operators to run it.